1. Field of the Invention
The present invention generally relates to systems and methods for forecasting the future availability of one or more resources, including but not limited to online display advertising opportunities.
2. Background
The Internet has emerged as a powerful advertising tool. It is commonplace to see advertisements on many Web sites. For example, advertisements may be displayed on search results pages generated by Web search engines and may be targeted to individuals based upon search terms submitted by the individuals. Other Web sites, such as news and sports Web sites, may provide space for display advertisements. Publishers of these Web sites may sell advertising space to advertisers to offset the costs associated with operating the Web sites as well as to turn a profit.
To place an advertisement, an advertiser may enter into a contract with a proprietor of an ad serving system to serve a number of advertisement impressions. An impression corresponds to the display of an advertisement to a user. For example, the advertiser may purchase 10,000 impressions for $100 and may specify a particular target audience, such as users that like sports.
When preparing to enter a contract, the advertiser (or an agent thereof) may interact with a system that allows the advertiser to specify the types of advertising opportunities in which the advertiser is interested so as to determine the availability thereof. This may involve selecting one or more target attributes from among a set of attributes and then specifying a target value for each of the target attributes. The target attributes may include, for example, attributes associated with the viewers that will view an impression (e.g., gender, age group), attributes associated with the content of the Web page on which the impression will be delivered, attributes involving the location of the impression on the Web page, a time period during which the impression will be served, and the like.
To determine the availability of future advertising opportunities that match the specified target attributes, a process sometimes referred as inventory forecasting may be used. Within the context of online display advertising, inventory forecasting generally involves using historical data regarding the actual delivery of different types of ad impressions to users to train a forecasting model which can then be used to predict when certain types of advertising opportunities will arise in the future. If a proprietor of an ad serving system can accurately forecast when advertising opportunities will arise, then the proprietor can more successfully monetize such advertising opportunities. For example, accurate inventory forecasting can enable the proprietor to guarantee the delivery of a certain volume of ad impressions to certain types of users during a particular time frame. It is therefore important to proprietors of ad serving systems to utilize an inventory forecasting model that is as accurate as possible.
In some cases, the availability of advertising opportunities is strongly shaped by particular events. For example, a Web site that publishes sports-related information (e.g., YAHOO! Sports) may experience a sudden and massive increase in online visits during the Super Bowl or other popular sporting event. If such event-driven spikes in online traffic could be accurately forecast, then a correspondingly large number of advertising opportunities could be monetized.
Since many high-traffic events are scheduled to occur at known dates and times, there would appear to be great potential to improve forecasting accuracy for event-driven inventory if calendar information could be embedded into model training and forecasting. However, there are various problems that must be solved in order to achieve this. One of the most challenging problems is that different events may exhibit a wide variety of durations and alignments. For example, the same annual event may be shifted in time by a couple of days or weeks from one year to another. Furthermore, traditional inventory forecasting frameworks lack the power to deal with calendar knowledge. Typically, calendar effects have to be removed from the historical signal before training. Another problem is that some conventional inventory forecasting frameworks utilize parametric forecasting models. However, such models do not perform well in predicting complex signals, especially when such signals are event-driven.